Load libraries
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(car)
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
Read datasets
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')
AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')
AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')
Create data frames for each model.
# Define aggregate variables.
All_Gross_M1_log <- log(AllSubs_NeuralActivation$Gross_US_M1)
All_Theaters_M1 <- AllSubs_NeuralActivation$Theaters_US_M1
Comedy_Gross_M1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_M1)
Comedy_Theaters_M1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_M1
Horror_Gross_M1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_M1)
Horror_Theaters_M1 <- AllSubs_NeuralActivation_Horror$Theaters_US_M1
M1_df <- data.frame(All_Gross_M1_log, All_Theaters_M1)
M1_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_Theaters_M1)
M1_H_df <- data.frame(Horror_Gross_M1_log, Horror_Theaters_M1)
# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled
Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled
Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled
M2_df <- data.frame(All_Gross_M1_log, All_PA, All_NA)
M2_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA)
M2_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA)
# Define ISC variables.
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC
Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC
Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC
# Define models.
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
M5_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M5_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M5_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
# Define whole variables.
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole
Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole
Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole
# Define models.
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole)
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole)
M7_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M7_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
Comedy_AIns_whole, Comedy_MPFC_whole)
M7_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
Horror_AIns_whole, Horror_MPFC_whole)
# Define onset variables.
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset
Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset
Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset
# Define models.
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset)
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset)
M9_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M9_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_onset, Comedy_MPFC_onset)
M9_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_onset, Horror_MPFC_onset)
# Define middle variables.
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle
Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle
Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle
# Define models.
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle)
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle)
M11_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M11_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
Comedy_AIns_middle, Comedy_MPFC_middle)
M11_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
Horror_AIns_middle, Horror_MPFC_middle)
# Define offset variables.
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset
Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset
Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset
# Define models.
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset)
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset)
M13_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M13_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
Comedy_AIns_offset, Comedy_MPFC_offset)
M13_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
Horror_AIns_offset, Horror_MPFC_offset)
M14_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset)
M14_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_middle, Comedy_MPFC_offset)
M14_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_middle, Horror_MPFC_offset)
Notes:
- Have note removed outliers from data.
Neuroforecasting: First Month US.
M1: Aggregste data
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(Theaters_US_M1) +
Type:scale(Theaters_US_M1), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.77903 -0.23205 -0.05965 0.21883 0.83396
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.21275 0.12503 137.673 < 2e-16 ***
Typecomedy -0.03297 0.16727 -0.197 0.845
scale(Theaters_US_M1) 0.96069 0.17747 5.413 1.13e-05 ***
Typecomedy:scale(Theaters_US_M1) -0.24037 0.20114 -1.195 0.243
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4365 on 26 degrees of freedom
Multiple R-squared: 0.7846, Adjusted R-squared: 0.7597
F-statistic: 31.56 on 3 and 26 DF, p-value: 8.065e-09
R2m R2c
[1,] 0.7655209 0.7655209
[1] 41.10136



M2: Affective data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.33061 -0.68235 0.03616 0.55621 1.50581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.7431 0.6865 25.847 <2e-16 ***
Typecomedy -1.6486 1.1230 -1.468 0.155
scale(Pos_arousal_scaled) -0.3041 0.5202 -0.585 0.564
scale(Neg_arousal_scaled) -0.5096 0.4649 -1.096 0.284
Typecomedy:scale(Pos_arousal_scaled) 0.7048 0.5681 1.241 0.227
Typecomedy:scale(Neg_arousal_scaled) -0.3017 1.1007 -0.274 0.786
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8524 on 24 degrees of freedom
Multiple R-squared: 0.2415, Adjusted R-squared: 0.08352
F-statistic: 1.529 on 5 and 24 DF, p-value: 0.2184
R2m R2c
[1,] 0.2085769 0.2085769
[1] 82.85859



M3: Aggregate and affective data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.33061 -0.68235 0.03616 0.55621 1.50581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.7431 0.6865 25.847 <2e-16 ***
Typecomedy -1.6486 1.1230 -1.468 0.155
scale(Pos_arousal_scaled) -0.3041 0.5202 -0.585 0.564
scale(Neg_arousal_scaled) -0.5096 0.4649 -1.096 0.284
Typecomedy:scale(Pos_arousal_scaled) 0.7048 0.5681 1.241 0.227
Typecomedy:scale(Neg_arousal_scaled) -0.3017 1.1007 -0.274 0.786
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8524 on 24 degrees of freedom
Multiple R-squared: 0.2415, Adjusted R-squared: 0.08352
F-statistic: 1.529 on 5 and 24 DF, p-value: 0.2184
R2m R2c
[1,] 0.2085769 0.2085769
[1] 82.85859
M4: ISC data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_ISC) + scale(AIns_ISC) +
scale(MPFC_ISC) + Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) +
Type:scale(MPFC_ISC), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.6449 -0.6801 -0.1485 0.5868 1.4765
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.43054 0.25030 69.639 <2e-16 ***
Typecomedy -0.46878 0.34267 -1.368 0.185
scale(NAcc_ISC) 0.14030 0.24521 0.572 0.573
scale(AIns_ISC) -0.01868 0.26216 -0.071 0.944
scale(MPFC_ISC) -0.05224 0.32252 -0.162 0.873
Typecomedy:scale(NAcc_ISC) -0.40918 0.35499 -1.153 0.261
Typecomedy:scale(AIns_ISC) 0.05005 0.35826 0.140 0.890
Typecomedy:scale(MPFC_ISC) 0.31327 0.38358 0.817 0.423
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9206 on 22 degrees of freedom
Multiple R-squared: 0.1889, Adjusted R-squared: -0.06915
F-statistic: 0.7321 on 7 and 22 DF, p-value: 0.6472
R2m R2c
[1,] 0.1501694 0.1501694
[1] 88.87055



M5: ISC data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_ISC) +
scale(AIns_ISC) + scale(MPFC_ISC) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) + Type:scale(MPFC_ISC),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.72828 -0.24181 -0.01225 0.13953 0.77659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.80988 0.45741 36.750 < 2e-16 ***
Typecomedy -0.01687 0.74729 -0.023 0.982271
scale(Theaters_US_M1) 1.01015 0.22697 4.451 0.000403 ***
scale(Pos_arousal_scaled) -0.48858 0.31307 -1.561 0.138170
scale(Neg_arousal_scaled) 0.05774 0.31379 0.184 0.856325
scale(NAcc_ISC) -0.05501 0.14031 -0.392 0.700206
scale(AIns_ISC) -0.15752 0.14431 -1.091 0.291218
scale(MPFC_ISC) 0.04026 0.18069 0.223 0.826510
Typecomedy:scale(Theaters_US_M1) -0.25989 0.27060 -0.960 0.351144
Typecomedy:scale(Pos_arousal_scaled) 0.56469 0.34396 1.642 0.120154
Typecomedy:scale(Neg_arousal_scaled) -0.49637 0.71663 -0.693 0.498462
Typecomedy:scale(NAcc_ISC) 0.13862 0.20819 0.666 0.514999
Typecomedy:scale(AIns_ISC) 0.06913 0.19968 0.346 0.733698
Typecomedy:scale(MPFC_ISC) -0.11206 0.22123 -0.507 0.619403
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4812 on 16 degrees of freedom
Multiple R-squared: 0.8388, Adjusted R-squared: 0.7079
F-statistic: 6.405 on 13 and 16 DF, p-value: 0.0003889
R2m R2c
[1,] 0.741687 0.741687
[1] 52.39564



M6: Neural whole data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_whole) + scale(AIns_whole) +
scale(MPFC_whole) + Type:scale(NAcc_whole) + Type:scale(AIns_whole) +
Type:scale(MPFC_whole), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.35757 -0.50465 -0.08527 0.51664 1.98973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.32455 0.31829 54.431 <2e-16 ***
Typecomedy 0.05934 0.45424 0.131 0.897
scale(NAcc_whole) -0.53847 0.32957 -1.634 0.117
scale(AIns_whole) 0.44028 0.37757 1.166 0.256
scale(MPFC_whole) 0.11712 0.31802 0.368 0.716
Typecomedy:scale(NAcc_whole) 0.39747 0.43101 0.922 0.366
Typecomedy:scale(AIns_whole) 0.21229 0.56055 0.379 0.709
Typecomedy:scale(MPFC_whole) -0.09317 0.39390 -0.237 0.815
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8842 on 22 degrees of freedom
Multiple R-squared: 0.2519, Adjusted R-squared: 0.0138
F-statistic: 1.058 on 7 and 22 DF, p-value: 0.4217
R2m R2c
[1,] 0.2034257 0.2034257
[1] 86.44776



M7: Neural whole data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_whole) +
scale(AIns_whole) + scale(MPFC_whole) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_whole) + Type:scale(AIns_whole) + Type:scale(MPFC_whole),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.75210 -0.17062 -0.04619 0.17582 0.86458
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.820726 0.493062 34.115 2.26e-16 ***
Typecomedy 0.042263 0.757801 0.056 0.95622
scale(Theaters_US_M1) 0.927804 0.246430 3.765 0.00169 **
scale(Pos_arousal_scaled) -0.300491 0.465969 -0.645 0.52815
scale(Neg_arousal_scaled) 0.152888 0.343094 0.446 0.66185
scale(NAcc_whole) -0.179193 0.204203 -0.878 0.39319
scale(AIns_whole) 0.149404 0.239286 0.624 0.54118
scale(MPFC_whole) -0.009189 0.266106 -0.035 0.97288
Typecomedy:scale(Theaters_US_M1) -0.266261 0.277711 -0.959 0.35194
Typecomedy:scale(Pos_arousal_scaled) 0.409818 0.489846 0.837 0.41512
Typecomedy:scale(Neg_arousal_scaled) -0.600991 0.757529 -0.793 0.43918
Typecomedy:scale(NAcc_whole) 0.240124 0.273630 0.878 0.39318
Typecomedy:scale(AIns_whole) 0.049768 0.360733 0.138 0.89199
Typecomedy:scale(MPFC_whole) 0.011639 0.299914 0.039 0.96952
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4732 on 16 degrees of freedom
Multiple R-squared: 0.8442, Adjusted R-squared: 0.7175
F-statistic: 6.667 on 13 and 16 DF, p-value: 0.0003061
R2m R2c
[1,] 0.7492828 0.7492828
[1] 51.38494



M8: Neural onset data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_onset) + scale(AIns_onset) +
scale(MPFC_onset) + Type:scale(NAcc_onset) + Type:scale(AIns_onset) +
Type:scale(MPFC_onset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.54818 -0.64671 0.07108 0.59724 1.36025
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.505333 0.296081 59.124 <2e-16 ***
Typecomedy -0.524802 0.386474 -1.358 0.188
scale(NAcc_onset) -0.189713 0.341844 -0.555 0.585
scale(AIns_onset) 0.009848 0.382899 0.026 0.980
scale(MPFC_onset) 0.155665 0.310544 0.501 0.621
Typecomedy:scale(NAcc_onset) 0.605660 0.421334 1.437 0.165
Typecomedy:scale(AIns_onset) -0.200098 0.500120 -0.400 0.693
Typecomedy:scale(MPFC_onset) 0.192033 0.449838 0.427 0.674
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.875 on 22 degrees of freedom
Multiple R-squared: 0.2674, Adjusted R-squared: 0.03427
F-statistic: 1.147 on 7 and 22 DF, p-value: 0.3712
R2m R2c
[1,] 0.216831 0.216831
[1] 85.81866



M9: Neural onset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_onset) + scale(MPFC_onset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_onset) + Type:scale(AIns_onset) + Type:scale(MPFC_onset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.39595 -0.27695 0.00788 0.22706 0.65812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.6933 0.4902 36.092 < 2e-16 ***
Typecomedy -0.5994 0.7054 -0.850 0.4080
scale(Theaters_US_M1) 1.0783 0.1883 5.728 3.11e-05 ***
scale(Pos_arousal_scaled) -0.2504 0.2905 -0.862 0.4015
scale(Neg_arousal_scaled) -0.3947 0.3158 -1.250 0.2293
scale(NAcc_onset) -0.4064 0.1609 -2.526 0.0225 *
scale(AIns_onset) -0.6175 0.2323 -2.658 0.0172 *
scale(MPFC_onset) 0.1763 0.1531 1.151 0.2666
Typecomedy:scale(Theaters_US_M1) -0.3773 0.2166 -1.742 0.1007
Typecomedy:scale(Pos_arousal_scaled) 0.2705 0.3201 0.845 0.4106
Typecomedy:scale(Neg_arousal_scaled) 0.2729 0.6183 0.441 0.6648
Typecomedy:scale(NAcc_onset) 0.5517 0.2186 2.523 0.0226 *
Typecomedy:scale(AIns_onset) 0.6186 0.2769 2.233 0.0401 *
Typecomedy:scale(MPFC_onset) -0.2801 0.2228 -1.257 0.2268
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3911 on 16 degrees of freedom
Multiple R-squared: 0.8935, Adjusted R-squared: 0.807
F-statistic: 10.33 on 13 and 16 DF, p-value: 1.904e-05
R2m R2c
[1,] 0.822395 0.822395
[1] 39.95427



M10: Neural middle data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(NAcc_middle) +
Type:scale(AIns_middle) + Type:scale(MPFC_middle), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.3915 -0.4466 0.1007 0.3873 1.1738
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.557276 0.264600 66.354 <2e-16 ***
Typecomedy -0.181788 0.372557 -0.488 0.6304
scale(NAcc_middle) -0.373031 0.346221 -1.077 0.2930
scale(AIns_middle) 0.007648 0.257133 0.030 0.9765
scale(MPFC_middle) -0.383172 0.239549 -1.600 0.1240
Typecomedy:scale(NAcc_middle) -0.040661 0.412866 -0.098 0.9224
Typecomedy:scale(AIns_middle) 0.815077 0.415339 1.962 0.0625 .
Typecomedy:scale(MPFC_middle) 0.571557 0.347535 1.645 0.1143
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7837 on 22 degrees of freedom
Multiple R-squared: 0.4123, Adjusted R-squared: 0.2253
F-statistic: 2.205 on 7 and 22 DF, p-value: 0.074
R2m R2c
[1,] 0.3473471 0.3473471
[1] 79.20621



M11: Neural middle data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_middle) + Type:scale(AIns_middle) + Type:scale(MPFC_middle),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.64812 -0.23692 0.00575 0.14246 0.95153
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.690510 0.486655 34.296 < 2e-16 ***
Typecomedy -0.007343 0.709246 -0.010 0.99187
scale(Theaters_US_M1) 1.248793 0.343965 3.631 0.00225 **
scale(Pos_arousal_scaled) -0.430717 0.316851 -1.359 0.19288
scale(Neg_arousal_scaled) 0.007413 0.317422 0.023 0.98166
scale(NAcc_middle) 0.312436 0.306492 1.019 0.32318
scale(AIns_middle) 0.098045 0.159056 0.616 0.54629
scale(MPFC_middle) 0.105902 0.191564 0.553 0.58802
Typecomedy:scale(Theaters_US_M1) -0.629131 0.367410 -1.712 0.10614
Typecomedy:scale(Pos_arousal_scaled) 0.445077 0.359614 1.238 0.23370
Typecomedy:scale(Neg_arousal_scaled) -0.750351 0.741702 -1.012 0.32675
Typecomedy:scale(NAcc_middle) -0.377060 0.343398 -1.098 0.28844
Typecomedy:scale(AIns_middle) 0.226057 0.350700 0.645 0.52833
Typecomedy:scale(MPFC_middle) -0.109742 0.256655 -0.428 0.67465
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4691 on 16 degrees of freedom
Multiple R-squared: 0.8469, Adjusted R-squared: 0.7225
F-statistic: 6.807 on 13 and 16 DF, p-value: 0.0002701
R2m R2c
[1,] 0.7531617 0.7531617
[1] 50.85846



M12: Neural offset data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(NAcc_offset) +
Type:scale(AIns_offset) + Type:scale(MPFC_offset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.5994 -0.5186 -0.0776 0.3276 1.7949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.39804 0.25755 67.552 <2e-16 ***
Typecomedy -0.40239 0.36911 -1.090 0.287
scale(NAcc_offset) -0.28867 0.28169 -1.025 0.317
scale(AIns_offset) 0.16721 0.24494 0.683 0.502
scale(MPFC_offset) 0.20047 0.33370 0.601 0.554
Typecomedy:scale(NAcc_offset) 0.01794 0.44416 0.040 0.968
Typecomedy:scale(AIns_offset) -0.17271 0.49837 -0.347 0.732
Typecomedy:scale(MPFC_offset) -0.37187 0.42214 -0.881 0.388
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9026 on 22 degrees of freedom
Multiple R-squared: 0.2204, Adjusted R-squared: -0.02772
F-statistic: 0.8883 on 7 and 22 DF, p-value: 0.532
R2m R2c
[1,] 0.1765553 0.1765553
[1] 87.68491



M13: Neural offset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_offset) + Type:scale(AIns_offset) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.72052 -0.21534 -0.05562 0.16478 0.96215
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.84671 0.54606 30.851 1.1e-15 ***
Typecomedy -0.14113 0.75493 -0.187 0.85405
scale(Theaters_US_M1) 1.11029 0.37047 2.997 0.00853 **
scale(Pos_arousal_scaled) -0.22652 0.40800 -0.555 0.58645
scale(Neg_arousal_scaled) 0.15853 0.54061 0.293 0.77311
scale(NAcc_offset) -0.02963 0.16116 -0.184 0.85644
scale(AIns_offset) 0.08824 0.17258 0.511 0.61611
scale(MPFC_offset) -0.20723 0.35957 -0.576 0.57240
Typecomedy:scale(Theaters_US_M1) -0.39008 0.39094 -0.998 0.33323
Typecomedy:scale(Pos_arousal_scaled) 0.36240 0.43503 0.833 0.41708
Typecomedy:scale(Neg_arousal_scaled) -0.70653 0.80755 -0.875 0.39457
Typecomedy:scale(NAcc_offset) 0.01809 0.25068 0.072 0.94336
Typecomedy:scale(AIns_offset) 0.12076 0.29862 0.404 0.69128
Typecomedy:scale(MPFC_offset) 0.22578 0.38817 0.582 0.56891
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4803 on 16 degrees of freedom
Multiple R-squared: 0.8394, Adjusted R-squared: 0.709
F-statistic: 6.434 on 13 and 16 DF, p-value: 0.0003785
R2m R2c
[1,] 0.7425612 0.7425612
[1] 52.28065



M14: Sequence Model
Call:
lm(formula = rank(Gross_Total_US_bytheater) ~ Type + scale(Nacc_peak) +
Type:scale(Nacc_peak), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy")),
Gross_Total_US_bytheater = Gross_Total_US/Total_Theaters_US,
Gross_US_M1_bytheater = Gross_US_M1/Theaters_US_W4_num) %>%
group_by(Trailer) %>% mutate(Nacc_peak = max(c_across(c(NAcc_onset,
NAcc_middle, NAcc_offset)), na.rm = TRUE)))
Residuals:
Min 1Q Median 3Q Max
-14.8851 -6.5955 0.1789 6.7344 14.8768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.017 2.438 6.981 2.06e-07 ***
Typecomedy -3.007 3.336 -0.902 0.376
scale(Nacc_peak) -0.600 2.557 -0.235 0.816
Typecomedy:scale(Nacc_peak) 2.060 3.411 0.604 0.551
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 9.08 on 26 degrees of freedom
Multiple R-squared: 0.0462, Adjusted R-squared: -0.06386
F-statistic: 0.4198 on 3 and 26 DF, p-value: 0.7403
M15: Sequence Model 2
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_onset) + scale(MPFC_middle) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled) + Type:scale(NAcc_onset) +
Type:scale(AIns_onset) + Type:scale(MPFC_middle), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.60763 -0.22255 -0.00175 0.21260 0.59443
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.18801 0.49790 36.530 < 2e-16 ***
Typecomedy -0.69745 0.75999 -0.918 0.3716
scale(Theaters_US_M1) 0.67691 0.09529 7.104 1.77e-06 ***
scale(Pos_arousal_scaled) 0.08062 0.28983 0.278 0.7842
scale(Neg_arousal_scaled) -0.62840 0.30845 -2.037 0.0575 .
scale(NAcc_onset) -0.37491 0.15472 -2.423 0.0268 *
scale(AIns_onset) -0.52899 0.22047 -2.399 0.0282 *
scale(MPFC_middle) -0.24136 0.12773 -1.890 0.0760 .
Typecomedy:scale(Pos_arousal_scaled) -0.09169 0.31712 -0.289 0.7760
Typecomedy:scale(Neg_arousal_scaled) 0.97290 0.71542 1.360 0.1916
Typecomedy:scale(NAcc_onset) 0.68195 0.24402 2.795 0.0124 *
Typecomedy:scale(AIns_onset) 0.45269 0.24897 1.818 0.0867 .
Typecomedy:scale(MPFC_middle) 0.44599 0.20170 2.211 0.0410 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3907 on 17 degrees of freedom
Multiple R-squared: 0.8871, Adjusted R-squared: 0.8075
F-statistic: 11.13 on 12 and 17 DF, p-value: 8.397e-06
R2m R2c
[1,] 0.8216611 0.8216611
[1] 39.70758
there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif
Type scale(Theaters_US_M1) scale(Pos_arousal_scaled)
28.252711 1.724939 15.959411
scale(Neg_arousal_scaled) scale(NAcc_onset) scale(AIns_onset)
18.075599 4.548107 9.234779
scale(MPFC_middle) Type:scale(Pos_arousal_scaled) Type:scale(Neg_arousal_scaled)
3.099778 10.931294 20.417830
Type:scale(NAcc_onset) Type:scale(AIns_onset) Type:scale(MPFC_middle)
7.323326 8.268350 4.263094
Type <- AllSubs_NeuralActivation$Type
M14_df <- data.frame(Type, All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset)
ggpairs(M14_df,
columnLabels = c("Type","Gross M1","PA",
"NA","NAcc on","AIns mid", "MPFC of"),
aes(color=Type, alpha = 0.5), upper = list(continuous = wrap("cor", size = 2.5) ))

---
title: "R Notebook"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load libraries
```{r}
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(car)
```


```{r, warning = FALSE, message = FALSE}
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
```

# Read datasets
```{r}
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')

AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')

AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')

```


# Create data frames for each model.
```{r}
# Define aggregate variables. 
All_Gross_M1_log <- log(AllSubs_NeuralActivation$Gross_US_M1)
All_Theaters_M1 <- AllSubs_NeuralActivation$Theaters_US_M1

Comedy_Gross_M1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_M1)
Comedy_Theaters_M1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_M1

Horror_Gross_M1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_M1)
Horror_Theaters_M1 <- AllSubs_NeuralActivation_Horror$Theaters_US_M1
  
M1_df <- data.frame(All_Gross_M1_log, All_Theaters_M1) 
M1_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_Theaters_M1) 
M1_H_df <- data.frame(Horror_Gross_M1_log, Horror_Theaters_M1) 

# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled

Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled

Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled

M2_df <- data.frame(All_Gross_M1_log, All_PA, All_NA) 
M2_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA) 
M2_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA) 
```

```{r}
# Define ISC variables. 
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC

Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC

Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC

# Define models. 
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 

M5_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M5_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M5_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 
```

```{r}
# Define whole variables. 
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole

Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole

Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole

# Define models. 
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole) 
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole) 

M7_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M7_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
                      Comedy_AIns_whole, Comedy_MPFC_whole) 
M7_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
                      Horror_AIns_whole, Horror_MPFC_whole) 
```

```{r}
# Define onset variables. 
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset

Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset

Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset

# Define models. 
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset) 
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset) 

M9_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M9_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_onset, Comedy_MPFC_onset) 
M9_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_onset, Horror_MPFC_onset) 
```

```{r}
# Define middle variables. 
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle

Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle

Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle

# Define models. 
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle) 
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle) 

M11_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M11_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
                      Comedy_AIns_middle, Comedy_MPFC_middle) 
M11_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
                      Horror_AIns_middle, Horror_MPFC_middle) 
```

```{r}
# Define offset variables. 
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset

Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset

Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset

# Define models. 
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset) 
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset) 

M13_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M13_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
                      Comedy_AIns_offset, Comedy_MPFC_offset) 
M13_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
                      Horror_AIns_offset, Horror_MPFC_offset) 
```

```{r}

M14_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset) 
M14_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_middle, Comedy_MPFC_offset) 
M14_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_middle, Horror_MPFC_offset) 
```

# Notes: 
 - Have note removed outliers from data.

# Neuroforecasting: First Month US.
## M1: Aggregste data 
```{r, echo = FALSE}
M1 <- lm(log(Gross_US_M1) ~ Type +
         + scale(Theaters_US_M1)
         #+ Weeks_avg_per_theater
         + Type:scale(Theaters_US_M1)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M1)
r.squaredGLMM(M1)
AIC(M1)

# Create pairs plot. 
ggpairs(M1_df)
ggpairs(M1_C_df)
ggpairs(M1_H_df)
```



## M2: Affective data alone
```{r, echo = FALSE}
M2 <- lm(log(Gross_US_M1) ~ Type 
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M2)
r.squaredGLMM(M2)
AIC(M2)

# Create pairs plot. 
ggpairs(M2_df)
ggpairs(M2_C_df)
ggpairs(M2_H_df)
```

## M3: Aggregate and affective data alone
```{r, echo = FALSE}
M3 <- lm(log(Gross_US_M1) ~ Type 
         #+ scale(Theaters_US_M1)
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         #+ Type:scale(Theaters_US_M1)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M3)
r.squaredGLMM(M3)
AIC(M3)
```

# M4: ISC data alone
```{r, echo = FALSE}
M4 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_ISC) 
              + scale(AIns_ISC) 
              + scale(MPFC_ISC) 
              + Type:scale(NAcc_ISC) 
              + Type:scale(AIns_ISC) 
              + Type:scale(MPFC_ISC) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M4)
r.squaredGLMM(M4)
AIC(M4)

# Create pairs plot. 
ggpairs(M4_df)
ggpairs(M4_C_df)
ggpairs(M4_H_df)
```

# M5: ISC data + affective data + behavioral data
```{r, echo = FALSE}
M5 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1) 
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_ISC) 
             + scale(AIns_ISC) 
             + scale(MPFC_ISC) 
             + Type:scale(Theaters_US_M1) 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_ISC) 
             + Type:scale(AIns_ISC) 
             + Type:scale(MPFC_ISC)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M5)
r.squaredGLMM(M5)
AIC(M5)

# Create pairs plot. 
ggpairs(M5_df)
ggpairs(M5_C_df)
ggpairs(M5_H_df)
```

# M6: Neural whole data alone
```{r, echo = FALSE}
M6 <- lm(log(Gross_US_M1) ~ Type + 
              #+ Theaters_US_W1_num 
              + scale(NAcc_whole) 
              + scale(AIns_whole) 
              + scale(MPFC_whole) 
              + Type:scale(NAcc_whole) 
              + Type:scale(AIns_whole) 
              + Type:scale(MPFC_whole) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M6)
r.squaredGLMM(M6)
AIC(M6)

# Create pairs plot. 
ggpairs(M6_df)
ggpairs(M6_C_df)
ggpairs(M6_H_df)
```

# M7: Neural whole data + affective data + behavioral data
```{r, echo = FALSE}
M7 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_whole) 
             + scale(AIns_whole) 
             + scale(MPFC_whole) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_whole) 
             + Type:scale(AIns_whole) 
             + Type:scale(MPFC_whole)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M7)
r.squaredGLMM(M7)
AIC(M7)

# Create pairs plot. 
ggpairs(M7_df)
ggpairs(M7_C_df)
ggpairs(M7_H_df)
```

# M8: Neural onset data alone
```{r, echo = FALSE}
M8 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_onset) 
              + scale(AIns_onset) 
              + scale(MPFC_onset) 
              + Type:scale(NAcc_onset) 
              + Type:scale(AIns_onset) 
              + Type:scale(MPFC_onset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M8)
r.squaredGLMM(M8)
AIC(M8)

# Create pairs plot. 
ggpairs(M8_df)
ggpairs(M8_C_df)
ggpairs(M8_H_df)
```

# M9: Neural onset data + affective data + behavioral data
```{r, echo = FALSE}
M9 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_onset) 
             + scale(AIns_onset) 
             + scale(MPFC_onset) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_onset) 
             + Type:scale(MPFC_onset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M9)
r.squaredGLMM(M9)
AIC(M9)

# Create pairs plot. 
ggpairs(M9_df)
ggpairs(M9_C_df)
ggpairs(M9_H_df)
```

# M10: Neural middle data alone
```{r, echo = FALSE}
M10 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_middle) 
              + scale(AIns_middle) 
              + scale(MPFC_middle) 
              + Type:scale(NAcc_middle) 
              + Type:scale(AIns_middle) 
              + Type:scale(MPFC_middle) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M10)
r.squaredGLMM(M10)
AIC(M10)

# Create pairs plot. 
ggpairs(M10_df)
ggpairs(M10_C_df)
ggpairs(M10_H_df)
```

# M11: Neural middle data + affective data + behavioral data
```{r, echo = FALSE}
M11 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_middle) 
             + scale(AIns_middle) 
             + scale(MPFC_middle) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_middle) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_middle)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M11)
r.squaredGLMM(M11)
AIC(M11)

# Create pairs plot. 
ggpairs(M11_df)
ggpairs(M11_C_df)
ggpairs(M11_H_df)
```

# M12: Neural offset data alone
```{r, echo = FALSE}
M12 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_offset) 
              + scale(AIns_offset) 
              + scale(MPFC_offset) 
              + Type:scale(NAcc_offset) 
              + Type:scale(AIns_offset) 
              + Type:scale(MPFC_offset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M12)
r.squaredGLMM(M12)
AIC(M12)

# Create pairs plot. 
ggpairs(M12_df)
ggpairs(M12_C_df)
ggpairs(M12_H_df)
```

# M13: Neural offset data + affective data + behavioral data
```{r, echo = FALSE}
M13 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_offset) 
             + scale(AIns_offset) 
             + scale(MPFC_offset) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_offset) 
             + Type:scale(AIns_offset) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M13)
r.squaredGLMM(M13)
AIC(M13)

# Create pairs plot. 
ggpairs(M13_df)
ggpairs(M13_C_df)
ggpairs(M13_H_df)
```

# M14: Sequence Model
```{r, echo = FALSE}
M14 <- lm(rank(Gross_Total_US_bytheater) ~ Type 
             #+ scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             #+ scale(Pos_arousal_scaled) 
             #+ scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(Nacc_peak) 
             # + scale(AIns_middle) 
             # + scale(MPFC_offset) 
             #+ Type:scale(Theaters_US_M1)
             #+ Type:scale(Pos_arousal_scaled)
             #+ Type:scale(Neg_arousal_scaled)
             + Type:scale(Nacc_peak) 
             # + Type:scale(AIns_middle) 
             # + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy")),
                                                          Gross_Total_US_bytheater = Gross_Total_US/Total_Theaters_US,
                                                          Gross_US_M1_bytheater = Gross_US_M1/Theaters_US_W4_num) %>%
            group_by(Trailer) %>%
            mutate(Nacc_peak = max(c_across(c(NAcc_onset, NAcc_middle, NAcc_offset)), na.rm = TRUE)))
summary(M14)
r.squaredGLMM(M14)
AIC(M14)
```

# M15: Sequence Model 2
```{r, echo = FALSE}
 # Effects become more significant if we remove 'Theater_num' predictor... we can do that with the 
# 'GrossOverTheaters' variable, however MPFC looks a bit funny.  
M15 <- lm(log(Gross_US_M1) ~ Type
             + scale(Theaters_US_M1)
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_onset) 
             + scale(AIns_onset) 
             + scale(MPFC_middle) 
             #+ Type:scale(Theaters_US_M1) # Should we have a theaters interaction? 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_onset) 
             + Type:scale(MPFC_middle)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M15)
r.squaredGLMM(M15)
AIC(M15)

# Create pairs plot. 
#ggpairs(M14_df)
#ggpairs(M14_C_df)
#ggpairs(M14_H_df)
vif(M15)
```


```{r}
Type <- AllSubs_NeuralActivation$Type
M14_df <- data.frame(Type, All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset) 
ggpairs(M14_df,
        columnLabels = c("Type","Gross M1","PA",
                 "NA","NAcc on","AIns mid", "MPFC of"),
        aes(color=Type, alpha = 0.5), upper = list(continuous = wrap("cor", size = 2.5) ))

```

